KR102367831B1 - An artificial intelligence apparatus for the self-diagnosis using log data and artificial intelligence model and method for the same - Google Patents

Abstract

The present invention provides a sensing unit for collecting a driving log including information on external environmental factors and operating states of an artificial intelligence device, a memory for storing data corresponding to the driving log, and artificial intelligence data corresponding to the driving log. Provided is an artificial intelligence device comprising a processor that provides information to a model to obtain information on whether the artificial intelligence device corresponds to a normal category or a failure symptom category, and performs control according to the acquired information.

Description

Artificial intelligence device and method for fault diagnosis using operation log and artificial intelligence model

The present invention relates to an artificial intelligence device capable of diagnosing a failure of an artificial intelligence device by inputting a driving log of the artificial intelligence device into an artificial intelligence model.

Artificial intelligence (artificial intelligence) is a field of computer engineering and information technology that studies how computers can do the thinking, learning, and self-development that can be done by human intelligence. This means that it can be imitated.

In addition, artificial intelligence does not exist by itself, but is directly or indirectly related to other fields of computer science. In particular, in modern times, attempts are being made to introduce artificial intelligence elements in various fields of information technology and use them to solve problems in that field.

On the other hand, technologies for recognizing and learning the surrounding situation using artificial intelligence, providing information desired by the user in a desired form, or performing an operation or function desired by the user are being actively researched.

And, an electronic device that provides such various operations and functions may be called an artificial intelligence device.

On the other hand, artificial intelligence devices may fail due to various causes. However, there is a problem in that it is difficult to determine the failure symptoms and causes of the malfunctioning artificial intelligence device without a highly skilled expert.

In addition, since the individual environment in which the artificial intelligence device is used is different for each user, fault diagnosis in consideration of the individual use environment is required.

Accordingly, there is an increasing need for artificial intelligence devices capable of self-diagnosing failures and judging failure symptoms according to individual usage environments.

SUMMARY OF THE INVENTION The present invention aims to solve the above and other problems.

An object of the present invention is to provide an artificial intelligence device capable of diagnosing a failure of an artificial intelligence device without the help of an expert.

An object of the present invention is to provide an artificial intelligence device capable of learning an artificial intelligence model using information sensed by the artificial intelligence device and diagnosing a failure of the artificial intelligence device using the learned artificial intelligence model.

An object of the present invention is to provide an artificial intelligence device capable of performing fault diagnosis in consideration of an individual environment by re-learning an artificial intelligence model in consideration of the individual environment in which the artificial intelligence device is used.

An object of the present invention is to provide an artificial intelligence device capable of efficiently managing information sensed by the artificial intelligence device.

An embodiment of the present invention provides a sensing unit for collecting a driving log including information on external environmental factors and operating states of an artificial intelligence device, a memory for storing data corresponding to the driving log, and artificial intelligence data corresponding to the driving log. Provided is an artificial intelligence device including a processor that obtains information on whether the artificial intelligence device corresponds to a normal category or a failure symptom category by providing it to the intelligent model, and performs control according to the acquired information.

An embodiment of the present invention provides an artificial intelligence device, which is a neural network trained by an artificial intelligence model by labeling training data corresponding to a driving log and information on a normal category or a malfunction symptom category.

An embodiment of the present invention provides data corresponding to a driving log to an artificial intelligence model, and based on a classification value output using the data provided by the artificial intelligence model, the driving log is in at least one normal category or at least one or more It provides an artificial intelligence device including a processor for acquiring classification result information on which category of the failure symptom categories falls under.

An embodiment of the present invention provides an artificial intelligence device including a processor that deletes data corresponding to a driving log stored in a memory when it is determined that the artificial intelligence device corresponds to a normal category.

An embodiment of the present invention provides an artificial intelligence device including a processor for storing data corresponding to a driving log stored in the memory as a special log in the memory when it is determined that the artificial intelligence device corresponds to a failure symptom category.

An embodiment of the present invention provides an artificial intelligence device including a processor that allows an access request to a specific log stored in a memory when a failure corresponding to the acquired information is received.

In an embodiment of the present invention, when a failure corresponding to information acquired within a preset time is not received, data corresponding to a driving log is labeled with information on a normal category and provided to an artificial intelligence model. Provide intelligent devices.

In an embodiment of the present invention, when a failure corresponding to information obtained within a preset time is received, information on a failure symptom category is labeled in data corresponding to a driving log and provided to an artificial intelligence model. Provide intelligent devices.

An embodiment of the present invention provides a sensing unit including a dust sensor that collects fine dust concentration, which is information on external environmental factors of an artificial intelligence device, and a motor sensor that collects rotations per minute of a motor, which is operation state information of the artificial intelligence device. and the data corresponding to the operation log provides an artificial intelligence device, which is a feature vector representing the fine dust concentration collected by the dust sensor and the number of revolutions per minute of the motor collected by the motor sensor.

An embodiment of the present invention provides an artificial intelligence model with data of a time section corresponding to a driving log to provide an artificial intelligence device including a processor for obtaining information on whether the artificial intelligence device corresponds to a normal category or a failure symptom category. to provide.

In addition, an embodiment of the present invention includes the steps of collecting a driving log including information on external environmental elements and operating states of the artificial intelligence device, and providing data corresponding to the driving log to the artificial intelligence model to ensure that the artificial intelligence device is normal. It provides a fault diagnosis method comprising the steps of acquiring information on whether a category or a fault symptom category corresponds to and performing control according to the acquired information.

In addition, an embodiment of the present invention provides a failure diagnosis method in which the artificial intelligence model is a neural network trained by labeling training data corresponding to all logs and information on a normal category or a failure symptom category.

In addition, an embodiment of the present invention provides data corresponding to the driving log to an artificial intelligence model, and based on a classification value output using the data provided by the artificial intelligence model, the driving log is in at least one normal category or It provides a failure diagnosis method comprising the step of obtaining classification result information on which category of at least one or more failure symptom categories falls under.

In addition, an embodiment of the present invention is a step of determining whether the artificial intelligence device corresponds to a normal category or a failure symptom category based on the acquired information, and when it is determined that the artificial intelligence device corresponds to a normal category, corresponding to the driving log It provides a fault diagnosis method comprising the step of deleting the data.

In addition, an embodiment of the present invention includes the steps of determining whether the artificial intelligence device corresponds to a normal category or a failure symptom category based on the obtained information, and when it is determined that the artificial intelligence device corresponds to a failure symptom category, the operation log is It provides a fault diagnosis method comprising the step of storing corresponding data as a specific log.

In addition, an embodiment of the present invention provides a step of obtaining a result value as to whether a failure corresponding to the acquired information has been received, and when a failure corresponding to the acquired information is received, allowing an access request to the stored specific log It provides a fault diagnosis method further comprising a step.

In addition, an embodiment of the present invention provides a step of obtaining a result value as to whether a failure corresponding to the acquired information has been received, and when a failure corresponding to the acquired information is not received within a preset time, corresponding to the operation log It provides a fault diagnosis method further comprising the step of labeling the data with information about the normal category and providing it to the artificial intelligence model.

In addition, an embodiment of the present invention includes the steps of obtaining a result value as to whether a failure corresponding to the acquired information has been received, and when a failure corresponding to the acquired information is received within a preset time, data corresponding to the operation log It provides a fault diagnosis method further comprising the step of labeling the information on the fault symptom category on the table and providing it to the artificial intelligence model.

In addition, an embodiment of the present invention includes the steps of collecting the fine dust concentration, which is information on external environmental factors of the artificial intelligence device, collecting the number of revolutions per minute of the motor, which is the operation state information of the artificial intelligence device, the fine dust concentration and Provided is a fault diagnosis method comprising the step of providing a feature vector representing the number of revolutions per minute of a motor to an artificial intelligence model to obtain information on whether an artificial intelligence device corresponds to a normal category or a fault symptom category.

In addition, an embodiment of the present invention provides the data of the time section corresponding to the driving log to the artificial intelligence model to diagnose a failure comprising the step of obtaining information on whether the artificial intelligence device corresponds to a normal category or a failure symptom category provide a way

According to an embodiment of the present invention, it is possible to diagnose a failure of an artificial intelligence device without the help of an expert.

Also, according to an embodiment of the present invention, an artificial intelligence model may be trained using information sensed by the artificial intelligence device, and a failure diagnosis of the artificial intelligence device may be performed using the learned artificial intelligence model.

In addition, according to an embodiment of the present invention, a failure diagnosis may be performed in consideration of an individual environment in which an artificial intelligence device is used.

In addition, according to an embodiment of the present invention, it is possible to efficiently manage the log by classifying only the driving log to be stored, without the need to store all driving logs for environmental factors and operating states in which the artificial intelligence device is used.

In addition, according to an embodiment of the present invention, it is possible to efficiently receive and repair a failure by enabling the identification of symptoms and causes of failure of an artificial intelligence device.

1 shows an AI device 100 according to an embodiment of the present invention.
2 shows an AI server 200 according to an embodiment of the present invention.
3 shows an AI system 1 according to an embodiment of the present invention.
4 is a view for explaining the sensing unit 140 according to an embodiment of the present invention.
5 is a diagram for explaining a method of generating an artificial intelligence model, according to an embodiment of the present invention.
6 is an operation flowchart illustrating a method for an artificial intelligence device to diagnose a failure using a driving log and an artificial intelligence model according to an embodiment of the present invention.
7 to 9 are diagrams for explaining data corresponding to a driving log learned by an artificial intelligence model of an artificial intelligence device according to an embodiment of the present invention.

Hereinafter, the embodiments disclosed in the present specification will be described in detail with reference to the accompanying drawings, but the same or similar components are assigned the same reference numbers regardless of reference numerals, and redundant description thereof will be omitted. The suffixes "module" and "part" for components used in the following description are given or mixed in consideration of only the ease of writing the specification, and do not have distinct meanings or roles by themselves. In addition, in describing the embodiments disclosed in the present specification, if it is determined that detailed descriptions of related known technologies may obscure the gist of the embodiments disclosed in this specification, the detailed description thereof will be omitted. In addition, the accompanying drawings are only for easy understanding of the embodiments disclosed in the present specification, and the technical idea disclosed herein is not limited by the accompanying drawings, and all changes included in the spirit and scope of the present invention , should be understood to include equivalents or substitutes.

Terms including an ordinal number such as 1st, 2nd, etc. may be used to describe various elements, but the elements are not limited by the terms. The above terms are used only for the purpose of distinguishing one component from another.

When an element is referred to as being “connected” or “connected” to another element, it is understood that it may be directly connected or connected to the other element, but other elements may exist in between. it should be On the other hand, when it is said that a certain element is "directly connected" or "directly connected" to another element, it should be understood that the other element does not exist in the middle.

<Artificial Intelligence (AI)>

Artificial intelligence refers to a field that studies artificial intelligence or methodologies that can create it, and machine learning refers to a field that defines various problems dealt with in the field of artificial intelligence and studies methodologies to solve them. do. Machine learning is also defined as an algorithm that improves the performance of a certain task through constant experience.

An artificial neural network (ANN) is a model used in machine learning, and may refer to an overall model having problem-solving ability, which is composed of artificial neurons (nodes) that form a network by combining synapses. An artificial neural network may be defined by a connection pattern between neurons of different layers, a learning process that updates model parameters, and an activation function that generates an output value.

The artificial neural network may include an input layer, an output layer, and optionally one or more hidden layers. Each layer includes one or more neurons, and the artificial neural network may include neurons and synapses connecting neurons. In the artificial neural network, each neuron may output a function value of an activation function for input signals, weights, and biases input through synapses.

Model parameters refer to parameters determined through learning, and include the weight of synaptic connections and the bias of neurons. In addition, the hyperparameter refers to a parameter that must be set before learning in a machine learning algorithm, and includes a learning rate, the number of iterations, a mini-batch size, an initialization function, and the like.

The purpose of learning the artificial neural network can be seen as determining the model parameters that minimize the loss function. The loss function may be used as an index for determining optimal model parameters in the learning process of the artificial neural network.

Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning according to a learning method.

Supervised learning refers to a method of training an artificial neural network in a state where a label for the training data is given, and the label is the correct answer (or result value) that the artificial neural network should infer when the training data is input to the artificial neural network. can mean Unsupervised learning may refer to a method of training an artificial neural network in a state where no labels are given for training data. Reinforcement learning can refer to a learning method in which an agent defined in an environment learns to select an action or sequence of actions that maximizes the cumulative reward in each state.

Among artificial neural networks, machine learning implemented as a deep neural network (DNN) including a plurality of hidden layers is also called deep learning (deep learning), and deep learning is a part of machine learning. Hereinafter, machine learning is used in a sense including deep learning.

<Robot>

A robot can mean a machine that automatically handles or operates a task given by its own capabilities. In particular, a robot having a function of recognizing an environment and performing an operation by self-judgment may be referred to as an intelligent robot.

Robots can be classified into industrial, medical, home, military, etc. depending on the purpose or field of use.

The robot may be provided with a driving unit including an actuator or a motor to perform various physical operations such as moving the robot joints. In addition, the movable robot includes a wheel, a brake, a propeller, and the like in the driving unit, and may travel on the ground or fly in the air through the driving unit.

<Self-Driving>

Autonomous driving refers to a technology that drives itself, and an autonomous driving vehicle refers to a vehicle that travels without or with minimal manipulation of a user.

For example, autonomous driving includes technology for maintaining a driving lane, technology for automatically adjusting speed such as adaptive cruise control, technology for automatically driving along a predetermined route, technology for automatically setting a route when a destination is set, etc. All of these can be included.

The vehicle includes a vehicle having only an internal combustion engine, a hybrid vehicle having both an internal combustion engine and an electric motor, and an electric vehicle having only an electric motor, and may include not only automobiles, but also trains, motorcycles, and the like.

In this case, the autonomous vehicle can be viewed as a robot having an autonomous driving function.

<Extended Reality (XR: eXtended Reality)>

The extended reality is a generic term for virtual reality (VR), augmented reality (AR), and mixed reality (MR). VR technology provides only CG images of objects or backgrounds in the real world, AR technology provides virtual CG images on top of images of real objects, and MR technology is a computer that mixes and combines virtual objects in the real world. graphic technology.

MR technology is similar to AR technology in that it shows both real and virtual objects. However, there is a difference in that in AR technology, a virtual object is used in a form that complements a real object, whereas in MR technology, a virtual object and a real object are used with equal characteristics.

XR technology can be applied to HMD (Head-Mount Display), HUD (Head-Up Display), mobile phone, tablet PC, laptop, desktop, TV, digital signage, etc. can be called

1 shows an AI device 100 according to an embodiment of the present invention.

AI device 100 is TV, projector, mobile phone, smart phone, desktop computer, notebook computer, digital broadcasting terminal, PDA (personal digital assistants), PMP (portable multimedia player), navigation, tablet PC, wearable device, set-top box (STB) ), a DMB receiver, a radio, a washing machine, a refrigerator, a desktop computer, a digital signage, a robot, a vehicle, etc., may be implemented as a stationary device or a movable device.

Referring to FIG. 1 , the terminal 100 includes a communication unit 110 , an input unit 120 , a learning processor 130 , a sensing unit 140 , an output unit 150 , a memory 170 and a processor 180 , etc. may include

The communication unit 110 may transmit/receive data to and from external devices such as other AI devices 100a to 100e or the AI server 200 using wired/wireless communication technology. For example, the communication unit 110 may transmit and receive sensor information, a user input, a learning model, a control signal, and the like with external devices.

In this case, the communication technology used by the communication unit 110 includes GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), LTE (Long Term Evolution), 5G, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity) ), Bluetooth™, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), ZigBee, NFC (Near Field Communication), and the like.

The input unit 120 may acquire various types of data.

In this case, the input unit 120 may include a camera for inputting an image signal, a microphone for receiving an audio signal, a user input unit for receiving information from a user, and the like. Here, by treating the camera or the microphone as a sensor, a signal obtained from the camera or the microphone may be referred to as sensing data or sensor information.

The input unit 120 may acquire training data for model training and input data to be used when acquiring an output using the training model. The input unit 120 may acquire raw input data, and in this case, the processor 180 or the learning processor 130 may extract an input feature by preprocessing the input data.

The learning processor 130 may train a model composed of an artificial neural network by using the training data. Here, the learned artificial neural network may be referred to as a learning model. The learning model may be used to infer a result value with respect to new input data other than the training data, and the inferred value may be used as a basis for a decision to perform a certain operation.

In this case, the learning processor 130 may perform AI processing together with the learning processor 240 of the AI server 200 .

In this case, the learning processor 130 may include a memory integrated or implemented in the AI device 100 . Alternatively, the learning processor 130 may be implemented using the memory 170 , an external memory directly coupled to the AI device 100 , or a memory maintained in an external device.

The sensing unit 140 may acquire at least one of internal information of the AI device 100 , information on the surrounding environment of the AI device 100 , and user information by using various sensors.

At this time, sensors included in the sensing unit 140 include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, and a lidar. , radar, etc.

The output unit 150 may generate an output related to sight, hearing, or touch.

In this case, the output unit 150 may include a display unit that outputs visual information, a speaker that outputs auditory information, and a haptic module that outputs tactile information.

The memory 170 may store data supporting various functions of the AI device 100 . For example, the memory 170 may store input data obtained from the input unit 120 , learning data, a learning model, a learning history, and the like.

The processor 180 may determine at least one executable operation of the AI device 100 based on information determined or generated using a data analysis algorithm or a machine learning algorithm. In addition, the processor 180 may control the components of the AI device 100 to perform the determined operation.

To this end, the processor 180 may request, search, receive, or utilize the data of the learning processor 130 or the memory 170, and perform a predicted operation or an operation determined to be preferable among the at least one executable operation. It is possible to control the components of the AI device 100 to execute.

In this case, when the connection of the external device is required to perform the determined operation, the processor 180 may generate a control signal for controlling the corresponding external device and transmit the generated control signal to the corresponding external device.

The processor 180 may obtain intention information with respect to a user input, and determine a user's requirement based on the obtained intention information.

In this case, the processor 180 uses at least one of a speech to text (STT) engine for converting a voice input into a character string or a natural language processing (NLP) engine for obtaining intention information of a natural language, Intention information corresponding to the input may be obtained.

At this time, at least one of the STT engine and the NLP engine may be configured as an artificial neural network, at least a part of which is learned according to a machine learning algorithm. And, at least one or more of the STT engine or the NLP engine is learned by the learning processor 130, or learned by the learning processor 240 of the AI server 200, or learned by distributed processing thereof. it could be

The processor 180 collects history information including the user's feedback on the operation contents or operation of the AI device 100 and stores it in the memory 170 or the learning processor 130, or the AI server 200 It can be transmitted to an external device. The collected historical information may be used to update the learning model.

The processor 180 may control at least some of the components of the AI device 100 in order to drive an application program stored in the memory 170 . Furthermore, in order to drive the application program, the processor 180 may operate two or more of the components included in the AI device 100 in combination with each other.

2 shows an AI server 200 according to an embodiment of the present invention.

Referring to FIG. 2 , the AI server 200 may refer to a device that trains an artificial neural network using a machine learning algorithm or uses a learned artificial neural network. Here, the AI server 200 may be configured with a plurality of servers to perform distributed processing, and may be defined as a 5G network. In this case, the AI server 200 may be included as a part of the AI device 100 to perform at least a part of AI processing together.

The AI server 200 may include a communication unit 210 , a memory 230 , a learning processor 240 , a processor 260 , and the like.

The communication unit 210 may transmit/receive data to and from an external device such as the AI device 100 .

The memory 230 may include a model storage unit 231 . The model storage unit 231 may store a model (or artificial neural network, 231a) being trained or learned through the learning processor 240 .

The learning processor 240 may train the artificial neural network 231a using the training data. The learning model may be used while being mounted on the AI server 200 of the artificial neural network, or may be used while being mounted on an external device such as the AI device 100 .

The learning model may be implemented in hardware, software, or a combination of hardware and software. When a part or all of the learning model is implemented in software, one or more instructions constituting the learning model may be stored in the memory 230 .

The processor 260 may infer a result value with respect to new input data using the learning model, and may generate a response or a control command based on the inferred result value.

3 shows an AI system 1 according to an embodiment of the present invention.

Referring to FIG. 3 , the AI system 1 includes at least one of an AI server 200 , a robot 100a , an autonomous vehicle 100b , an XR device 100c , a smart phone 100d , or a home appliance 100e . It is connected to the cloud network 10 . Here, the robot 100a to which the AI technology is applied, the autonomous driving vehicle 100b, the XR device 100c, the smart phone 100d, or the home appliance 100e may be referred to as AI devices 100a to 100e.

The cloud network 10 may constitute a part of the cloud computing infrastructure or may refer to a network existing in the cloud computing infrastructure. Here, the cloud network 10 may be configured using a 3G network, a 4G or Long Term Evolution (LTE) network, or a 5G network.

That is, each of the devices 100a to 100e and 200 constituting the AI system 1 may be connected to each other through the cloud network 10 . In particular, each of the devices 100a to 100e and 200 may communicate with each other through the base station, but may also directly communicate with each other without passing through the base station.

The AI server 200 may include a server performing AI processing and a server performing an operation on big data.

The AI server 200 includes at least one of the AI devices constituting the AI system 1, such as a robot 100a, an autonomous vehicle 100b, an XR device 100c, a smartphone 100d, or a home appliance 100e, and It is connected through the cloud network 10 and may help at least a part of AI processing of the connected AI devices 100a to 100e.

In this case, the AI server 200 may train an artificial neural network according to a machine learning algorithm on behalf of the AI devices 100a to 100e, and directly store the learning model or transmit it to the AI devices 100a to 100e.

At this time, the AI server 200 receives input data from the AI devices 100a to 100e, infers a result value with respect to the input data received using the learning model, and provides a response or control command based on the inferred result value. It can be generated and transmitted to the AI devices 100a to 100e.

Alternatively, the AI devices 100a to 100e may infer a result value with respect to input data using a direct learning model, and generate a response or a control command based on the inferred result value.

Hereinafter, various embodiments of the AI devices 100a to 100e to which the above-described technology is applied will be described. Here, the AI devices 100a to 100e shown in FIG. 3 can be viewed as specific examples of the AI device 100 shown in FIG. 1 .

<AI+Robot>

The robot 100a may be implemented as a guide robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, etc. to which AI technology is applied.

The robot 100a may include a robot control module for controlling an operation, and the robot control module may mean a software module or a chip implemented as hardware.

The robot 100a acquires state information of the robot 100a by using sensor information obtained from various types of sensors, detects (recognizes) surrounding environments and objects, generates map data, moves path and travels A plan may be determined, a response to a user interaction may be determined, or an action may be determined.

Here, the robot 100a may use sensor information obtained from at least one sensor among LiDAR, radar, and camera in order to determine a movement route and a travel plan.

The robot 100a may perform the above-described operations using a learning model composed of at least one artificial neural network. For example, the robot 100a may recognize a surrounding environment and an object using a learning model, and may determine an operation using the recognized surrounding environment information or object information. Here, the learning model may be directly learned from the robot 100a or learned from an external device such as the AI server 200 .

In this case, the robot 100a may perform an operation by generating a result using a direct learning model, but transmits sensor information to an external device such as the AI server 200 and receives the result generated accordingly to perform the operation You may.

The robot 100a determines a movement path and travel plan using at least one of map data, object information detected from sensor information, or object information obtained from an external device, and controls the driving unit to apply the determined movement path and travel plan. Accordingly, the robot 100a may be driven.

The map data may include object identification information for various objects disposed in a space in which the robot 100a moves. For example, the map data may include object identification information for fixed objects such as walls and doors and movable objects such as flowerpots and desks. In addition, the object identification information may include a name, a type, a distance, a location, and the like.

In addition, the robot 100a may perform an operation or drive by controlling the driving unit based on the user's control/interaction. In this case, the robot 100a may acquire intention information of an interaction according to a user's motion or voice utterance, determine a response based on the acquired intention information, and perform the operation.

<AI + Autonomous Driving>

The autonomous driving vehicle 100b may be implemented as a mobile robot, a vehicle, an unmanned aerial vehicle, etc. by applying AI technology.

The autonomous driving vehicle 100b may include an autonomous driving control module for controlling an autonomous driving function, and the autonomous driving control module may mean a software module or a chip implemented as hardware. The autonomous driving control module may be included as a component of the autonomous driving vehicle 100b, or may be configured and connected to the outside of the autonomous driving vehicle 100b as separate hardware.

The autonomous vehicle 100b obtains state information of the autonomous vehicle 100b using sensor information obtained from various types of sensors, detects (recognizes) surrounding environments and objects, generates map data, A movement route and a driving plan may be determined, or an operation may be determined.

Here, the autonomous driving vehicle 100b may use sensor information obtained from at least one sensor among a lidar, a radar, and a camera, similarly to the robot 100a, in order to determine a moving route and a driving plan.

In particular, the autonomous vehicle 100b may receive sensor information from external devices to recognize an environment or object for an area where the field of view is obscured or an area over a certain distance, or receive information recognized directly from external devices. .

The autonomous vehicle 100b may perform the above-described operations by using a learning model composed of at least one artificial neural network. For example, the autonomous driving vehicle 100b may recognize a surrounding environment and an object using a learning model, and may determine a driving route using the recognized surrounding environment information or object information. Here, the learning model may be directly learned from the autonomous vehicle 100b or learned from an external device such as the AI server 200 .

In this case, the autonomous vehicle 100b may generate a result by using the direct learning model and perform the operation, but it operates by transmitting sensor information to an external device such as the AI server 200 and receiving the result generated accordingly. can also be performed.

The autonomous vehicle 100b uses at least one of map data, object information detected from sensor information, or object information obtained from an external device to determine a movement path and a driving plan, and controls the driving unit to determine the movement path and driving The autonomous vehicle 100b may be driven according to a plan.

The map data may include object identification information for various objects disposed in a space (eg, a road) in which the autonomous vehicle 100b travels. For example, the map data may include object identification information for fixed objects such as street lights, rocks, and buildings, and movable objects such as vehicles and pedestrians. In addition, the object identification information may include a name, a type, a distance, a location, and the like.

Also, the autonomous vehicle 100b may perform an operation or drive by controlling the driving unit based on the user's control/interaction. In this case, the autonomous vehicle 100b may acquire intention information of an interaction according to a user's motion or voice utterance, determine a response based on the obtained intention information, and perform the operation.

<AI+XR>

The XR device 100c is AI technology applied, so a head-mount display (HMD), a head-up display (HUD) provided in a vehicle, a television, a mobile phone, a smart phone, a computer, a wearable device, a home appliance, and a digital signage , a vehicle, a stationary robot, or a mobile robot.

The XR device 100c analyzes 3D point cloud data or image data acquired through various sensors or from an external device to generate location data and attribute data for 3D points, thereby providing information on surrounding space or real objects. It can be obtained and output by rendering the XR object to be output. For example, the XR apparatus 100c may output an XR object including additional information on the recognized object to correspond to the recognized object.

The XR apparatus 100c may perform the above-described operations using a learning model composed of at least one artificial neural network. For example, the XR apparatus 100c may recognize a real object from 3D point cloud data or image data using a learning model, and may provide information corresponding to the recognized real object. Here, the learning model may be directly learned from the XR device 100c or learned from an external device such as the AI server 200 .

In this case, the XR device 100c may perform an operation by generating a result using the direct learning model, but it transmits sensor information to an external device such as the AI server 200 and receives the result generated accordingly to perform the operation. can also be done

<AI+Robot+Autonomous Driving>

The robot 100a may be implemented as a guide robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, etc. to which AI technology and autonomous driving technology are applied.

The robot 100a to which AI technology and autonomous driving technology are applied may mean a robot having an autonomous driving function or a robot 100a that interacts with the autonomous driving vehicle 100b.

The robot 100a having an autonomous driving function may collectively refer to devices that move by themselves according to a given movement line without user's control or by determining a movement line by themselves.

The robot 100a with the autonomous driving function and the autonomous driving vehicle 100b may use a common sensing method to determine one or more of a moving route or a driving plan. For example, the robot 100a having an autonomous driving function and the autonomous driving vehicle 100b may determine one or more of a movement route or a driving plan by using information sensed through lidar, radar, and camera.

The robot 100a interacting with the autonomous driving vehicle 100b exists separately from the autonomous driving vehicle 100b and is linked to an autonomous driving function inside the autonomous driving vehicle 100b or connected to the autonomous driving vehicle 100b. It is possible to perform an operation associated with the user on board.

At this time, the robot 100a interacting with the autonomous driving vehicle 100b acquires sensor information on behalf of the autonomous driving vehicle 100b and provides it to the autonomous driving vehicle 100b, or obtains sensor information and obtains information about the surrounding environment or By generating object information and providing it to the autonomous driving vehicle 100b, the autonomous driving function of the autonomous driving vehicle 100b may be controlled or supported.

Alternatively, the robot 100a interacting with the autonomous driving vehicle 100b may monitor a user riding in the autonomous driving vehicle 100b or control a function of the autonomous driving vehicle 100b through interaction with the user. . For example, when it is determined that the driver is in a drowsy state, the robot 100a may activate an autonomous driving function of the autonomous driving vehicle 100b or assist in controlling a driving unit of the autonomous driving vehicle 100b. Here, the function of the autonomous driving vehicle 100b controlled by the robot 100a may include not only an autonomous driving function, but also a function provided by a navigation system or an audio system provided in the autonomous driving vehicle 100b.

Alternatively, the robot 100a interacting with the autonomous driving vehicle 100b may provide information or assist a function to the autonomous driving vehicle 100b from the outside of the autonomous driving vehicle 100b. For example, the robot 100a may provide traffic information including signal information to the autonomous driving vehicle 100b, such as a smart traffic light, or interact with the autonomous driving vehicle 100b, such as an automatic electric charger for an electric vehicle. You can also automatically connect an electric charger to the charging port.

<AI+Robot+XR>

The robot 100a may be implemented as a guide robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, a drone, etc. to which AI technology and XR technology are applied.

The robot 100a to which the XR technology is applied may mean a robot that is a target of control/interaction within an XR image. In this case, the robot 100a is distinguished from the XR device 100c and may be interlocked with each other.

When the robot 100a, which is the target of control/interaction within the XR image, obtains sensor information from sensors including a camera, the robot 100a or the XR device 100c generates an XR image based on the sensor information. and the XR apparatus 100c may output the generated XR image. In addition, the robot 100a may operate based on a control signal input through the XR device 100c or a user's interaction.

For example, the user can check the XR image corresponding to the viewpoint of the remotely linked robot 100a through an external device such as the XR device 100c, and adjust the autonomous driving path of the robot 100a through interaction or , control motion or driving, or check information of surrounding objects.

<AI+Autonomous Driving+XR>

The autonomous vehicle 100b may be implemented as a mobile robot, a vehicle, an unmanned aerial vehicle, etc. by applying AI technology and XR technology.

The autonomous driving vehicle 100b to which the XR technology is applied may mean an autonomous driving vehicle equipped with a means for providing an XR image or an autonomous driving vehicle subject to control/interaction within the XR image. In particular, the autonomous driving vehicle 100b, which is the target of control/interaction within the XR image, is distinguished from the XR device 100c and may be interlocked with each other.

The autonomous driving vehicle 100b having means for providing an XR image may obtain sensor information from sensors including a camera, and output an XR image generated based on the acquired sensor information. For example, the autonomous vehicle 100b may provide an XR object corresponding to a real object or an object in a screen to the passenger by outputting an XR image with a HUD.

In this case, when the XR object is output to the HUD, at least a portion of the XR object may be output to overlap the real object to which the passenger's gaze is directed. On the other hand, when the XR object is output to the display provided inside the autonomous vehicle 100b, at least a portion of the XR object may be output to overlap the object in the screen. For example, the autonomous vehicle 100b may output XR objects corresponding to objects such as a lane, other vehicles, traffic lights, traffic signs, two-wheeled vehicles, pedestrians, and buildings.

When the autonomous driving vehicle 100b, which is the subject of control/interaction in the XR image, acquires sensor information from sensors including a camera, the autonomous driving vehicle 100b or the XR device 100c performs An XR image is generated, and the XR apparatus 100c may output the generated XR image. In addition, the autonomous vehicle 100b may operate based on a control signal input through an external device such as the XR device 100c or a user's interaction.

First, we briefly explain artificial intelligence.

Artificial intelligence (AI) is a field of computer engineering and information technology that studies how computers can do the thinking, learning, and self-development that human intelligence can do. This means that the behavior can be imitated.

In addition, artificial intelligence does not exist by itself, but is directly or indirectly related to other fields of computer science. In particular, in modern times, attempts are being made to introduce artificial intelligence elements in various fields of information technology and use them to solve problems in that field.

Machine learning is a branch of artificial intelligence, a field of study that gives computers the ability to learn without an explicit program.

Specifically, machine learning can be said to be a technology to study and build a system and algorithms for learning based on empirical data, making predictions, and improving its own performance. Machine learning algorithms build specific models to make predictions or decisions based on input data, rather than executing strictly set static program instructions.

The term 'machine learning' may be used interchangeably with the term 'machine learning'.

With regard to how to classify data in machine learning, many machine learning algorithms have been developed. Decision trees, Bayesian networks, support vector machines (SVMs), and artificial neural networks (ANNs) are representative examples.

Decision tree is an analysis method that performs classification and prediction by charting decision rules in a tree structure.

The Bayesian network is a model that expresses the probabilistic relationship (conditional independence) between multiple variables in a graph structure. Bayesian networks are suitable for data mining through unsupervised learning.

The support vector machine is a model of supervised learning for pattern recognition and data analysis, and is mainly used for classification and regression analysis.

An artificial neural network is an information processing system in which a number of neurons called nodes or processing elements are connected in the form of a layer structure by modeling the operating principle of biological neurons and the connection relationship between neurons.

Artificial neural network is a model used in machine learning, and it is a statistical learning algorithm inspired by neural networks in biology (especially the brain in the central nervous system of animals) in machine learning and cognitive science.

Specifically, the artificial neural network may refer to an overall model having problem-solving ability by changing the bonding strength of synapses through learning in which artificial neurons (nodes) that form a network by combining synapses.

The term artificial neural network may be used interchangeably with the term neural network.

The artificial neural network may include a plurality of layers, and each of the layers may include a plurality of neurons. Also, the artificial neural network may include neurons and synapses connecting neurons.

In general, artificial neural networks calculate the output value from the following three factors: (1) the connection pattern between neurons in different layers (2) the learning process to update the weight of the connection (3) the weighted sum of the input received from the previous layer It can be defined by the activation function it creates.

The artificial neural network may include network models such as Deep Neural Network (DNN), Recurrent Neural Network (RNN), Bidirectional Recurrent Deep Neural Network (BRDNN), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN). , but is not limited thereto.

In this specification, the term 'layer' may be used interchangeably with the term 'layer'.

Artificial neural networks are divided into single-layer neural networks and multi-layer neural networks according to the number of layers.

A typical single-layer neural network consists of an input layer and an output layer.

In addition, a general multilayer neural network consists of an input layer, one or more hidden layers, and an output layer.

The input layer is a layer that receives external data. The number of neurons in the input layer is the same as the number of input variables, and the hidden layer is located between the input layer and the output layer. do. The output layer receives a signal from the hidden layer and outputs an output value based on the received signal. The input signal between neurons is multiplied by each connection strength (weight) and then summed. If the sum is greater than the neuron threshold, the neuron is activated and the output value obtained through the activation function is output.

Meanwhile, a deep neural network including a plurality of hidden layers between an input layer and an output layer may be a representative artificial neural network that implements deep learning, which is a type of machine learning technology.

Meanwhile, the term 'deep learning' may be used interchangeably with the term 'deep learning'.

The artificial neural network may be trained using training data. Here, learning refers to a process of determining parameters of an artificial neural network using learning data to achieve objectives such as classification, regression, or clustering of input data. can As a representative example of parameters of an artificial neural network, a weight applied to a synapse or a bias applied to a neuron may be mentioned.

The artificial neural network learned by the training data may classify or cluster input data according to a pattern of the input data.

Meanwhile, an artificial neural network trained using training data may be referred to as a trained model in the present specification.

The following describes the learning method of the artificial neural network.

Learning methods of artificial neural networks can be broadly classified into supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Supervised learning is a method of machine learning to infer a function from training data.

And among these inferred functions, outputting continuous values is called regression, and predicting and outputting the class of the input vector is called classification.

In supervised learning, an artificial neural network is trained in a state in which a label for training data is given.

Here, the label may mean a correct answer (or result value) that the artificial neural network should infer when training data is input to the artificial neural network.

In the present specification, when training data is input, the correct answer (or result value) that the artificial neural network must infer is called a label or labeling data.

Also, in this specification, setting a label on the training data for learning of the artificial neural network is called labeling the labeling data on the training data.

In this case, the training data and a label corresponding to the training data) constitute one training set, and may be input to the artificial neural network in the form of a training set.

On the other hand, training data represents a plurality of features, and labeling the training data may mean that the feature represented by the training data is labeled. In this case, the training data may represent the features of the input object in a vector form.

The artificial neural network may infer a function for the relationship between the training data and the labeling data by using the training data and the labeling data. In addition, parameters of the artificial neural network may be determined (optimized) through evaluation of the function inferred from the artificial neural network.

Unsupervised learning is a type of machine learning where no labels are given to training data.

Specifically, the unsupervised learning may be a learning method of learning the artificial neural network to find and classify patterns in the training data itself, rather than the association between the training data and the labels corresponding to the training data.

Examples of unsupervised learning include clustering or independent component analysis.

In this specification, the term 'clustering' may be used interchangeably with the term 'clustering'.

Examples of artificial neural networks using unsupervised learning include a generative adversarial network (GAN) and an autoencoder (AE).

A generative adversarial neural network is a machine learning method in which two different artificial intelligences, a generator and a discriminator, compete to improve performance.

In this case, the generator is a model that creates new data, and can generate new data based on the original data.

In addition, the discriminator is a model for recognizing patterns in data, and may play a role of discriminating whether input data is original data or new data generated by the generator.

And the generator learns by receiving the data that did not deceive the discriminator, and the discriminator can learn by receiving the deceived data from the generator. Accordingly, the generator may evolve to deceive the discriminator as best as possible, and the discriminator may evolve to distinguish the original data and the data generated by the generator well.

An autoencoder is a neural network that aims to reproduce the input itself as an output.

The auto-encoder includes an input layer, at least one hidden layer and an output layer.

In this case, since the number of nodes in the hidden layer is smaller than the number of nodes in the input layer, the dimension of data is reduced, and thus compression or encoding is performed.

Also, the data output from the hidden layer goes into the output layer. In this case, since the number of nodes of the output layer is greater than the number of nodes of the hidden layer, the dimension of data is increased, and decompression or decoding is performed accordingly.

On the other hand, the auto-encoder controls the neuron's connection strength through learning, so that the input data is expressed as hidden layer data. The hidden layer expresses information with fewer neurons than the input layer, and being able to reproduce the input data as an output may mean that the hidden layer found and expressed hidden patterns from the input data.

Semi-supervised learning is a type of machine learning, and may refer to a learning method using both labeled and unlabeled training data.

As one of the techniques of semi-supervised learning, there is a technique of inferring a label of unlabeled training data and then performing learning using the inferred label. can

Reinforcement learning is a theory that, given the environment in which the agent can decide what action to take at every moment, it can find the best way through experience without data.

Reinforcement learning may be mainly performed by a Markov Decision Process (MDP).

To explain the Markov decision process, firstly, an environment is given in which the information necessary for the agent to take the next action is given, secondly, how the agent will behave in that environment is defined, and thirdly, the agent is rewarded ( reward) and a penalty point for failure to do so, and fourthly, the optimal policy is derived by repeating experiences until the future reward reaches the highest point.

The structure of an artificial neural network is specified by the model configuration, activation function, loss function or cost function, learning algorithm, optimization algorithm, etc., and hyperparameters are It is set, and then the model parameter is set through learning and the content can be specified.

For example, factors determining the structure of an artificial neural network may include the number of hidden layers, the number of hidden nodes included in each hidden layer, an input feature vector, a target feature vector, and the like.

Hyperparameters include several parameters that must be initially set for learning, such as initial values of model parameters. And, the model parameter includes several parameters to be determined through learning.

For example, the hyperparameter may include an initial weight value between nodes, an initial bias value between nodes, a mini-batch size, a number of learning repetitions, a learning rate, and the like. In addition, the model parameters may include inter-node weights, inter-node biases, and the like.

The loss function may be used as an index (reference) for determining the optimal model parameter in the learning process of the artificial neural network. In artificial neural networks, learning refers to the process of manipulating model parameters to reduce the loss function, and the purpose of learning can be seen to determine the model parameters that minimize the loss function.

The loss function may mainly use a mean squared error (MSE) or a cross entropy error (CEE), but the present invention is not limited thereto.

The cross-entropy error can be used when the correct answer label is one-hot encoded. One-hot encoding is an encoding method in which the correct label value is set to 1 only for neurons corresponding to the correct answer, and the correct answer label value is set to 0 for neurons that do not have the correct answer.

In machine learning or deep learning, a learning optimization algorithm can be used to minimize the loss function, and the learning optimization algorithm includes gradient descent (GD), stochastic gradient descent (SGD), and momentum (Momentum). ), Nesterov Accelerate Gradient (NAG), Adagrad, AdaDelta, RMSProp, Adam, and Nadam.

Gradient descent is a technique that adjusts model parameters in a direction to reduce the loss function value by considering the gradient of the loss function in the current state.

The direction in which the model parameter is adjusted is referred to as a step direction, and the size to be adjusted is referred to as a step size.

In this case, the step size may mean a learning rate.

In the gradient descent method, a gradient is obtained by partial differentiation of the loss function into each model parameter, and the model parameters can be updated by changing the learning rate in the obtained gradient direction.

The stochastic gradient descent method is a technique in which the frequency of gradient descent is increased by dividing the training data into mini-batch and performing gradient descent for each mini-batch.

Adagrad, AdaDelta, and RMSProp are techniques to increase optimization accuracy by adjusting the step size in SGD. In SGD, momentum and NAG are techniques to increase optimization accuracy by adjusting the step direction. Adam is a technique to increase optimization accuracy by adjusting the step size and step direction by combining momentum and RMSProp. Nadam is a technique to increase optimization accuracy by adjusting the step size and step direction by combining NAG and RMSProp.

The learning speed and accuracy of an artificial neural network have a characteristic that it largely depends on hyperparameters as well as the structure of the artificial neural network and the type of learning optimization algorithm. Therefore, in order to obtain a good learning model, it is important not only to determine an appropriate artificial neural network structure and learning algorithm, but also to set appropriate hyperparameters.

Typically, hyperparameters are set to various values experimentally to train the artificial neural network, and as a result of learning, they are set to optimal values that provide stable learning speed and accuracy.

4 is a view for explaining the sensing unit 140 according to an embodiment of the present invention.

Referring to FIG. 4 , the sensing unit 140 of the artificial intelligence device 100 may collect driving logs including information on external environmental factors and operating states of the artificial intelligence device 100 by utilizing various sensors. . At least one of fine dust concentration, ultrafine dust concentration, ultrafine dust concentration, odor concentration value, air pollution degree, dust pollution degree, odor pollution degree, general cleanliness degree, humidity value, and temperature value information may be included. In addition, the operation log may include information on at least one of sensor monitoring ON/OFF status, signal lighting ON/OFF status, off/on reservation time, operation status, bedtime reservation time, and operation mode, which are information about the operation status. there is.

Also, the memory 170 of the artificial intelligence device 100 may store data corresponding to the driving log.

The sensing unit 140 may include a dust sensor 1401 that collects fine dust concentration, ultrafine dust concentration, or ultrafine dust concentration. The dust sensor 1401 may collect fine dust concentration, which is information on the external environmental factors of the artificial intelligence device 100 .

The sensing unit 140 may include a motor sensor 1402 that collects the number of revolutions per minute of the motor of the artificial intelligence device 100 when the artificial intelligence device 100 uses a motor. The motor sensor 1402 may collect the number of revolutions per minute of the motor, which is operation state information of the artificial intelligence device.

The sensing unit 140 may include an odor sensor 1403 that collects an odor concentration value or an air pollution level.

The sensing unit 140 may include a comprehensive cleanliness sensor 1404 that collects the overall cleanliness based on the dust pollution level and the odor pollution level.

The sensing unit 140 may include a humidity sensor 1405 that collects a humidity value.

The sensing unit 140 may include a temperature sensor 1406 that collects a temperature value.

The sensing unit 140 may include a monitoring sensor 1407 that collects a sensor monitoring ON/OFF state.

The sensing unit 140 may include a signaling sensor 1408 that collects a signal writing ON/OFF state.

The sensing unit 140 may include an off/on reservation sensor 1409 that collects an off or on reservation time of the artificial intelligence device 100 .

The sensing unit 140 may include a power sensor 1410 that collects information on whether the artificial intelligence device 100 is operating.

The sensing unit 140 may include a bedtime reservation sensor 1411 that collects the bedtime reservation time of the artificial intelligence device 100 .

The sensing unit 140 may include a driving mode sensor 1412 that collects information on the driving mode of the artificial intelligence device 100 .

5 is a diagram for explaining a method of generating an artificial intelligence model, according to an embodiment of the present invention.

The artificial intelligence model according to an embodiment of the present invention may be trained to predict whether an artificial intelligence device operates normally or a failure has occurred, or may be trained to predict what a failure symptom is.

The artificial intelligence model may be a neural network trained by labeling information on a normal category or a malfunction symptom category on training data corresponding to a driving log. On the other hand, the neural network can be called an artificial intelligence model. In addition, the artificial intelligence model may be trained in the artificial intelligence server 200 and mounted on the artificial intelligence device 100 . Also, the artificial intelligence device 100 may train the neural network.

First, a method of training the artificial intelligence device to predict whether it is normal or malfunctioning will be described with reference to FIG. 5A .

The artificial intelligence server 200 may train the neural network 501 by labeling the data corresponding to the driving log with information on the normal category or the malfunction symptom category.

The artificial intelligence server 200 labels the information on the normal category in training data corresponding to the driving log according to the normal scenario, and provides information on the failure symptom category in the training data corresponding to the driving log according to the failure scenario. By labeling, the neural network 501 can be trained.

Specifically, the artificial intelligence server 200 collects a driving log including information on the external environmental elements and operation state of the artificial intelligence device 100 using a sensor, and converts the collected driving log into data corresponding to the driving log. can be converted And, the artificial intelligence server 200 uses the data corresponding to the driving log as an input value, and outputs the state of the artificial intelligence device when the driving log of the artificial intelligence device is collected (operation within the normal category or operation within the fault symptom category) as an output value. can be used to train a neural network. Here, the state of the artificial intelligence device (operation within the normal category or operation within the fault symptom category) may be the correct answer that the neural network should infer using data corresponding to the driving log.

Accordingly, the artificial intelligence server 200 may label information on a normal category or a failure symptom category on data corresponding to the driving log and provide it to the neural network.

In this case, the neural network may infer a function for the correlation between the data corresponding to the driving log and the information on whether normal/failure by using the information on the normal category or the failure symptom category corresponding to the driving log. And, through evaluation of the function inferred from the neural network, parameters (weight, bias, etc.) of the neural network may be determined (optimized).

Meanwhile, the artificial intelligence server 200 may train a neural network using data of a predetermined time period.

Specifically, while the artificial intelligence device 100 is operating, the driving log including information on external environmental factors and operating states may be time-series collected, and thus data corresponding to the driving log is also time-series collected data. can

In this case, the artificial intelligence server 200 may train the neural network by separating time-series collected data according to a predetermined time interval, and labeling the separated data with information on a normal category or a failure symptom category.

For example, the artificial intelligence server 200 may separate time-series collected data in units of one second. And the artificial intelligence server 200 trains the neural network by labeling the information on the normal category or the failure symptom category on the data corresponding to the time interval of 1 second, and then sets the normal category or the data corresponding to the time interval of 1 second. You can train a neural network by labeling information about the failure symptom categories.

Meanwhile, the artificial intelligence server 200 can train a neural network using various types of artificial intelligence devices 100, driving logs collected through various types of sensors, and the state of the artificial intelligence devices when driving logs are generated. .

Here, the diversity of the types of the artificial intelligence device 100 means that the artificial intelligence device may be a robot 100a, an autonomous vehicle 100b, an XR device 100c, a smartphone 100d, or a home appliance 100e. can mean there is For example, the artificial intelligence device 100 may be an air purifier.

In addition, the variety of sensors may mean that there is at least one sensor capable of collecting environmental elements in which the artificial intelligence device is being used or the state in which the artificial intelligence device is operating. For example, when the artificial intelligence device 100 is an air purifier, the dust sensor 1401 , the motor sensor 1402 , the odor sensor 1403 , the general cleanliness sensor 1404 , the humidity sensor 1403 , and the temperature sensor At least one sensor among 1406, monitoring sensor 1407, signaling sensor 1408, off/on reservation sensor 1409, power sensor 1410, sleep reservation sensor 1411, and operation mode sensor 1412 It is possible to collect external environmental factors of the air purifier through

Next, referring to FIG. 5 (b), a method for training an artificial intelligence model to predict whether an artificial intelligence device corresponds to at least one normal category or at least one failure symptom category will be described. In addition, only points different from those described in FIG. 5 (a) will be described.

The artificial intelligence server 200 may label the data corresponding to the driving log with information on at least one normal category or at least one failure symptom category and provide it to the neural network. In this case, the classification value may be set for each of at least one normal category or at least one failure symptom category.

For example, the state of the AI device when the driving log of the AI device is collected can be classified into several categories. When the artificial intelligence device is an air purifier, it may be classified into at least one normal category or at least one malfunction symptom category for each individual use environment of the air purifier. For example, when the air purifier is used in an industrial site where a lot of dust is generated, the motor rotation number of the air purifier is higher than that of other use environments, but the dust concentration may not be significantly reduced, but it may be classified as a normal category. In addition, when the air purifier is used in an indoor home, the dust concentration will decrease inversely as the motor rotation speed increases, and it can be classified as a normal category. On the other hand, when the dust sensor of the air purifier malfunctions and the dust concentration is measured irregularly regardless of the motor rotation speed, it may be classified into a malfunction symptom category with a malfunction of the dust sensor. Alternatively, when the rotation speed of the motor of the air purifier is measured only constant regardless of the dust concentration, it may be classified as a malfunction symptom category in which the motor malfunctions.

In addition, the artificial intelligence server 200 receives data corresponding to the driving log as an input value, and the state of the artificial intelligence device when the driving log of the artificial intelligence device is collected (at least one normal category or at least one failure symptom category) A neural network can be trained using as an output value. Here, the state of the artificial intelligence device may be the correct answer that the neural network infers using data corresponding to the driving log. In addition, the output value may be a classification value set for each of at least one or more normal categories or at least one or more failure symptom categories. Accordingly, based on the classification value, it is possible to obtain classification result information on whether the operation log corresponds to any one of at least one normal category or at least one failure symptom category.

The artificial intelligence server 200 may label the data corresponding to the driving log with at least one normal category or at least one failure symptom category and provide it to the neural network.

In this case, the neural network can infer a function for the correlation between the data corresponding to the driving log and the data corresponding to the driving log and at least one normal category or at least one or more failure symptom categories, can And, through evaluation of the function inferred from the neural network, parameters (weight, bias, etc.) of the neural network may be determined (optimized).

In addition, the artificial intelligence server 200 separates the time-series collected data according to a time interval of a certain size, and labels the separated data with at least one normal category or at least one failure symptom category to train the neural network. there is.

On the other hand, the neural network trained in the above way can be called an artificial intelligence model.

Meanwhile, the artificial intelligence model may be mounted on the artificial intelligence device 100 .

Specifically, the artificial intelligence model may be implemented as hardware, software, or a combination of hardware and software. And when some or all of the artificial intelligence model is implemented in software, one or more instructions constituting the artificial intelligence model may be stored in the memory 170 of the artificial intelligence device.

Meanwhile, when the neural network is trained using data corresponding to the driving log, such data may be called training data corresponding to the driving log.

6 is an operation flowchart illustrating a method for an artificial intelligence device to diagnose a failure using a driving log and an artificial intelligence model according to an embodiment of the present invention.

The sensing unit 140 may collect a driving log including information on an external environmental element and an operating state of the artificial intelligence device 100 ( S601 ).

The processor 180 may provide data corresponding to the driving log to the AI model (S602).

The processor 180 may acquire information on whether the artificial intelligence device 100 corresponds to a normal category or a malfunction symptom category (S602).

Accordingly, the processor 180 may obtain information on whether the artificial intelligence device corresponds to a normal category or a failure symptom category by providing data corresponding to the driving log to the AI model.

The processor 180 may output information on whether the artificial intelligence device 100 corresponds to a normal category or a malfunction symptom category through the output unit 150 or transmit it to an external electronic device through the communication unit 110 .

The processor 180 may perform control according to the acquired information.

The processor 180 may control the artificial intelligence device 100 to output information on whether the artificial intelligence device 100 corresponds to a normal category or a malfunction symptom category through the output unit 150 . Accordingly, the user of the artificial intelligence device 100 may determine whether the artificial intelligence device 100 is operating normally or abnormally.

In addition, the processor 180 may control to transmit information on whether the artificial intelligence device 100 corresponds to a normal category or a malfunction symptom category to another artificial intelligence device or an artificial intelligence server through the communication unit 110 .

For example, the processor 180 transmits information about whether the artificial intelligence device 100 corresponds to a normal category or a malfunction symptom category through the communication unit 110 to a server of a service center operated by the manufacturer of the artificial intelligence device 100 . can be sent to

In addition, the processor 180 may control the operation of the artificial intelligence device 100 to be terminated when the artificial intelligence device 100 falls within the failure symptom category according to the acquired information.

Also, the processor 180 may control to store or delete data corresponding to the driving log according to the acquired information.

In addition, the processor 180 may determine whether the artificial intelligence device 100 corresponds to a normal category or a malfunction symptom category based on the acquired information ( S604 ).

Also, when it is determined that the artificial intelligence device 100 corresponds to the normal category, the processor 180 may delete data corresponding to the driving log stored in the memory 170 . Accordingly, it is possible to efficiently manage the driving log without storing all the driving logs.

In addition, when it is determined that the artificial intelligence device 100 corresponds to the failure symptom category, the processor 180 may store data corresponding to the driving log stored in the memory 170 as a special log. Therefore, it is possible to separately manage the log for identifying the cause of the failure as a special log without storing all the operation logs.

Meanwhile, the processor 180 may acquire a result value of whether a failure corresponding to the acquired information has been accepted. For example, a result value indicating whether a failure has been accepted may be input from the input unit 120 of the artificial intelligence device 100 , or a result value indicating whether a failure has been accepted may be obtained through the communication unit 110 .

Accordingly, the processor 180 may determine whether a failure has been accepted ( S605 ).

When a failure is received corresponding to information on whether the artificial intelligence device 100 corresponds to a normal category or a failure symptom category, the processor 180 may allow an access request to the specific log (S606). The processor 180 may receive an access request to the specific log through the input unit 120 of the artificial intelligence device 100 or may receive the request through the communication unit 110 . Accordingly, when a failure of the artificial intelligence device 100 is received, a specific log may also be provided, thereby facilitating diagnosis and repair of failure symptoms.

In addition, the processor 180 labels the information on the failure symptom category in the data corresponding to the operation log when a failure corresponding to the information on whether it corresponds to the normal category or the failure symptom category within a preset time is labeled with artificial intelligence. It can be provided to the model (S607). Accordingly, the processor 180 may re-learn the artificial intelligence model and secure an optimized artificial intelligence model according to the individual use environment of the artificial intelligence device.

On the other hand, the processor 180 labels the information on the normal category in the data corresponding to the operation log when the failure corresponding to the information on whether it corresponds to the normal category or the failure symptom category within the preset time is not received. It can be provided to the intelligence model (S608). Accordingly, the processor 180 may re-learn the artificial intelligence model and secure an optimized artificial intelligence model according to the individual use environment of the artificial intelligence device.

For example, when the air purifier is used in a dusty space, the motor speed of the air purifier is higher than that of the air purifier in other environments, but the dust concentration may not be significantly reduced. In this case, it may be determined that the air purifier falls within the category of failure symptoms, but if the failure is not received within the preset time, it may be regarded as corresponding to the normal category and the artificial intelligence model may be retrained.

7 to 9 are diagrams for explaining data corresponding to a driving log learned by an artificial intelligence model of an artificial intelligence device according to an embodiment of the present invention.

The sensing unit 140 of the artificial intelligence device 100 is a dust sensor 1401 that collects fine dust concentration, which is information on external environmental factors of the artificial intelligence device 100, and operation state information of the artificial intelligence device 100 may include a motor sensor 1402 that collects the number of revolutions per minute of the motor.

The processor 180 may acquire data corresponding to the driving log collected from the dust sensor 1401 and the motor sensor 1402 .

The processor 180 may store data corresponding to the acquired driving log in the memory 170 .

In more detail, the processor 180 may convert a driving log collected using the dust sensor 1401 and the motor sensor 1402 into data corresponding to the driving log. Here, the data corresponding to the operation log may be a feature vector indicating at least one of a fine dust concentration (㎍/M³) and a revolutions per minute (RPM) of the motor.

In this case, the processor 180 may convert the driving log into data of the same format as data used as training data of the artificial intelligence model. Meanwhile, when an artificial intelligence model is generated using data of a predetermined time period as training data, the processor 180 may provide data of a predetermined time period to the artificial intelligence model.

7 to 9 are examples of time series data corresponding to the operation log according to the normal/failure scenario of the air purifier, and the concentration of fine dust (㎍/M³) and the number of revolutions per minute of the motor ( RPM) change graph is shown.

7 is an exemplary change graph when the air purifier operates normally.

The fine dust concentration 701 decreases as the revolutions per minute 702 of the motor increases, and the revolutions per minute 702 of the motor decreases as the fine dust concentration 701 decreases. Therefore, time series data corresponding to the driving log according to the normal scenario can be labeled with information on the normal category and used for AI model learning.

8 is an exemplary change graph when a dust sensor malfunctions.

The dust sensor malfunctions, and it is measured that the fine dust concentration 801 sharply increases and then sharply decreases. Therefore, time series data corresponding to the driving log according to the dust sensor failure scenario can be labeled with information on the failure symptom (dust sensor failure) category and used for AI model learning.

9 is an exemplary change graph when a failure occurs in the motor.

Although the fine dust concentration 901 is decreasing, the number of revolutions per minute 902 of the motor, which should be decreased according to the fine dust concentration, is maintained constant regardless of a change in the fine dust concentration. Therefore, time series data corresponding to the driving log according to the motor failure scenario can be labeled with information on the failure symptom (motor failure) category and used for artificial intelligence model learning.

The processor 180 may obtain information on whether the artificial intelligence device corresponds to a normal category or a failure symptom category by providing a feature vector indicating the fine dust concentration and the number of revolutions per minute of the motor to the AI model.

The present invention described above can be implemented as computer-readable codes on a medium in which a program is recorded. The computer-readable medium includes all types of recording devices in which data readable by a computer system is stored. Examples of computer-readable media include Hard Disk Drive (HDD), Solid State Disk (SSD), Silicon Disk Drive (SDD), ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. There is this. In addition, the computer may include a processor 180 of the terminal.

Claims (20)

a sensing unit for collecting a driving log including information on external environmental factors and operating states of the artificial intelligence device;
a memory for storing time series data corresponding to the driving log; and
A processor configured to provide time series data corresponding to the driving log to an artificial intelligence model to obtain information on whether the artificial intelligence device corresponds to a normal category or a failure symptom category, and to perform control according to the acquired information ,
The artificial intelligence model is
It is a neural network trained by labeling information on a normal category or a failure symptom category on training time series data corresponding to the driving log,
The sensing unit,
a dust sensor that collects fine dust concentration, which is information on the external environmental factors, and a motor sensor that collects the number of revolutions per minute of the motor, which is information about the operating state,
the processor is
Diagnosing whether the artificial intelligence device is malfunctioning based on the time series data of the fine dust concentration and the time series data of the number of revolutions per minute of the motor
artificial intelligence devices. delete According to claim 1,
The processor is
Time series data corresponding to the driving log is provided to an artificial intelligence model, and based on a classification value output by the artificial intelligence model using the provided time series data, the driving log is at least one normal category or at least one failure Acquiring classification result information on which category of symptom categories falls under
artificial intelligence devices. According to claim 1,
The processor is
When it is determined that the artificial intelligence device corresponds to the normal category, deleting time series data corresponding to the driving log stored in the memory,
artificial intelligence devices. According to claim 1,
The processor is
When it is determined that the artificial intelligence device corresponds to the failure symptom category, time series data corresponding to the driving log stored in the memory is stored in the memory as a specific log,
artificial intelligence devices. 6. The method of claim 5,
The processor is
When a failure corresponding to the acquired information is received, an access request to the special log stored in the memory is allowed,
artificial intelligence devices. According to claim 1,
The processor is
When a failure corresponding to the acquired information is not received within a preset time, the time series data corresponding to the driving log is labeled with information on the normal category and provided to the artificial intelligence model,
artificial intelligence devices. The method of claim 1,
The processor is
When a failure corresponding to the acquired information is received within a preset time, information on a failure symptom category is labeled in time series data corresponding to the driving log and provided to the artificial intelligence model,
artificial intelligence devices. The method of claim 1,
Time series data corresponding to the operation log,
A feature vector representing time series data of the fine dust concentration and time series data of the number of revolutions per minute of the motor,
artificial intelligence devices. delete collecting, by a sensing unit, a driving log including information on external environmental factors and operating states of the artificial intelligence device;
obtaining, by the processor, time series data corresponding to the driving log to an artificial intelligence model to obtain information on whether the artificial intelligence device corresponds to a normal category or a failure symptom category; and
Including, by the processor, performing control according to the acquired information,
The artificial intelligence model is
It is a neural network trained by labeling information on normal categories or failure symptom categories on training time series data corresponding to the driving log,
The step of collecting the driving log includes:
collecting fine dust concentrations, which are information on the external environmental factors; and
Comprising the step of collecting the number of revolutions per minute of the motor that is information about the operating state,
The step of obtaining the information
diagnosing whether the artificial intelligence device is malfunctioning based on the time series data of the fine dust concentration and the time series data of the number of revolutions per minute of the motor
Fault diagnosis method using driving log and artificial intelligence model. delete 12. The method of claim 11,
The step of obtaining the information includes:
Time series data corresponding to the driving log is provided to an artificial intelligence model, and based on a classification value output by the artificial intelligence model using the provided time series data, the driving log is at least one normal category or at least one failure Including the step of obtaining classification result information for which category of symptom categories,
Fault diagnosis method using driving log and artificial intelligence model. 12. The method of claim 11,
The step of performing the control is
determining whether the artificial intelligence device corresponds to a normal category or a malfunction symptom category based on the acquired information; and
Comprising the step of deleting time series data corresponding to the driving log when it is determined that the artificial intelligence device corresponds to the normal category,
Fault diagnosis method using driving log and artificial intelligence model. 12. The method of claim 11,
The step of performing the control is
determining whether the artificial intelligence device corresponds to a normal category or a malfunction symptom category based on the acquired information; and
When it is determined that the artificial intelligence device corresponds to a failure symptom category, storing data corresponding to the driving log as a specific log,
Fault diagnosis method using driving log and artificial intelligence model. 16. The method of claim 15,
acquiring, by the processor, a result value of whether a failure corresponding to the acquired information has been accepted; and
Further comprising the step of allowing, by the processor, an access request to the stored specific log when a failure corresponding to the acquired information is received,
Fault diagnosis method using driving log and artificial intelligence model. 12. The method of claim 11,
acquiring, by the processor, a result value of whether a failure corresponding to the acquired information has been accepted; and
When the processor does not receive a failure corresponding to the acquired information within a preset time, labeling the time series data corresponding to the driving log information on the normal category and providing the information to the artificial intelligence model ,
Fault diagnosis method using driving log and artificial intelligence model. 12. The method of claim 11,
acquiring, by the processor, a result value of whether a failure corresponding to the acquired information has been accepted; and
When the processor receives a failure corresponding to the acquired information within a preset time, labeling information on a failure symptom category in time series data corresponding to the driving log and providing the information to the artificial intelligence model ,
Fault diagnosis method using driving log and artificial intelligence model. 12. The method of claim 11,
The step of obtaining the information includes:
Obtaining information on whether the artificial intelligence device corresponds to a normal category or a failure symptom category by providing a feature vector representing the time series data of the fine dust concentration and the time series data of the number of revolutions per minute to the artificial intelligence model containing,
Fault diagnosis method using driving log and artificial intelligence model. delete

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